code VS feature-engineering-tutorials

Compare code vs feature-engineering-tutorials and see what are their differences.

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code feature-engineering-tutorials
1 1
881 268
- 0.7%
0.0 0.0
4 months ago 12 days ago
Jupyter Notebook Jupyter Notebook
- GNU Affero General Public License v3.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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code

Posts with mentions or reviews of code. We have used some of these posts to build our list of alternatives and similar projects.

feature-engineering-tutorials

Posts with mentions or reviews of feature-engineering-tutorials. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-08.
  • How to balance multiple time series data?
    2 projects | /r/datascience | 8 Mar 2022
    I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.

What are some alternatives?

When comparing code and feature-engineering-tutorials you can also consider the following projects:

Shiny_Desktop_App - Deploy your R Shiny app(s) locally on Windows

jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!

gds_env - A containerised platform for Geographic Data Science

intro-to-python - An intro to Python & programming for wanna-be data scientists

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

dtreeviz - A python library for decision tree visualization and model interpretation.

ydata-quality - Data Quality assessment with one line of code

machine_learning_complete - A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

gastrodon - Visualize RDF data in Jupyter with Pandas

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

PRML - PRML algorithms implemented in Python